Yi Cui1, Jie Song2, Erqi Pollom2, Muthuraman Alagappan2, Hiroki Shirato3, Daniel T Chang4, Albert C Koong4, Ruijiang Li5. 1. Department of Radiation Oncology, Stanford University, Palo Alto, California; Global Institution for Collaborative Research and Education, Hokkaido University, Sapporo, Japan. 2. Department of Radiation Oncology, Stanford University, Palo Alto, California. 3. Global Institution for Collaborative Research and Education, Hokkaido University, Sapporo, Japan. 4. Department of Radiation Oncology, Stanford University, Palo Alto, California; Stanford Cancer Institute, Stanford, California. 5. Department of Radiation Oncology, Stanford University, Palo Alto, California; Global Institution for Collaborative Research and Education, Hokkaido University, Sapporo, Japan; Stanford Cancer Institute, Stanford, California. Electronic address: rli2@stanford.edu.
Abstract
PURPOSE: To identify prognostic biomarkers in pancreatic cancer using high-throughput quantitative image analysis. METHODS AND MATERIALS: In this institutional review board-approved study, we retrospectively analyzed images and outcomes for 139 locally advanced pancreatic cancer patients treated with stereotactic body radiation therapy (SBRT). The overall population was split into a training cohort (n=90) and a validation cohort (n=49) according to the time of treatment. We extracted quantitative imaging characteristics from pre-SBRT (18)F-fluorodeoxyglucose positron emission tomography, including statistical, morphologic, and texture features. A Cox proportional hazard regression model was built to predict overall survival (OS) in the training cohort using 162 robust image features. To avoid over-fitting, we applied the elastic net to obtain a sparse set of image features, whose linear combination constitutes a prognostic imaging signature. Univariate and multivariate Cox regression analyses were used to evaluate the association with OS, and concordance index (CI) was used to evaluate the survival prediction accuracy. RESULTS: The prognostic imaging signature included 7 features characterizing different tumor phenotypes, including shape, intensity, and texture. On the validation cohort, univariate analysis showed that this prognostic signature was significantly associated with OS (P=.002, hazard ratio 2.74), which improved upon conventional imaging predictors including tumor volume, maximum standardized uptake value, and total legion glycolysis (P=.018-.028, hazard ratio 1.51-1.57). On multivariate analysis, the proposed signature was the only significant prognostic index (P=.037, hazard ratio 3.72) when adjusted for conventional imaging and clinical factors (P=.123-.870, hazard ratio 0.53-1.30). In terms of CI, the proposed signature scored 0.66 and was significantly better than competing prognostic indices (CI 0.48-0.64, Wilcoxon rank sum test P<1e-6). CONCLUSION: Quantitative analysis identified novel (18)F-fluorodeoxyglucose positron emission tomography image features that showed improved prognostic value over conventional imaging metrics. If validated in large, prospective cohorts, the new prognostic signature might be used to identify patients for individualized risk-adaptive therapy.
PURPOSE: To identify prognostic biomarkers in pancreatic cancer using high-throughput quantitative image analysis. METHODS AND MATERIALS: In this institutional review board-approved study, we retrospectively analyzed images and outcomes for 139 locally advanced pancreatic cancerpatients treated with stereotactic body radiation therapy (SBRT). The overall population was split into a training cohort (n=90) and a validation cohort (n=49) according to the time of treatment. We extracted quantitative imaging characteristics from pre-SBRT (18)F-fluorodeoxyglucose positron emission tomography, including statistical, morphologic, and texture features. A Cox proportional hazard regression model was built to predict overall survival (OS) in the training cohort using 162 robust image features. To avoid over-fitting, we applied the elastic net to obtain a sparse set of image features, whose linear combination constitutes a prognostic imaging signature. Univariate and multivariate Cox regression analyses were used to evaluate the association with OS, and concordance index (CI) was used to evaluate the survival prediction accuracy. RESULTS: The prognostic imaging signature included 7 features characterizing different tumor phenotypes, including shape, intensity, and texture. On the validation cohort, univariate analysis showed that this prognostic signature was significantly associated with OS (P=.002, hazard ratio 2.74), which improved upon conventional imaging predictors including tumor volume, maximum standardized uptake value, and total legion glycolysis (P=.018-.028, hazard ratio 1.51-1.57). On multivariate analysis, the proposed signature was the only significant prognostic index (P=.037, hazard ratio 3.72) when adjusted for conventional imaging and clinical factors (P=.123-.870, hazard ratio 0.53-1.30). In terms of CI, the proposed signature scored 0.66 and was significantly better than competing prognostic indices (CI 0.48-0.64, Wilcoxon rank sum test P<1e-6). CONCLUSION: Quantitative analysis identified novel (18)F-fluorodeoxyglucose positron emission tomography image features that showed improved prognostic value over conventional imaging metrics. If validated in large, prospective cohorts, the new prognostic signature might be used to identify patients for individualized risk-adaptive therapy.
Authors: Linda M Pak; Mithat Gonen; Kenneth Seier; Vinod P Balachandran; Michael I D'Angelica; William R Jarnagin; T Peter Kingham; Peter J Allen; Richard K G Do; Amber L Simpson Journal: Abdom Radiol (NY) Date: 2018-08
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Authors: Adam A Dmytriw; Claudia Ortega; Reut Anconina; Ur Metser; Zhihui A Liu; Zijin Liu; Xuan Li; Thiparom Sananmuang; Eugene Yu; Sayali Joshi; John Waldron; Shao Hui Huang; Scott Bratman; Andrew Hope; Patrick Veit-Haibach Journal: Cancers (Basel) Date: 2022-06-24 Impact factor: 6.575